Chinese Journal of Agrometeorology ›› 2024, Vol. 45 ›› Issue (9): 1053-1066.doi: 10.3969/j.issn.1000-6362.2024.09.009

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The Rice Vulnerability to Sterile-type Chilling Disaster in China Based on Crop Model and Machine Learning

ZHANG Jing, ZHANG Zhao, ZHANG Liang-liang, CAO Juan, LUO Yu-chuan, HAN Ji-chong, TAO Fu-lu   

  1. 1. Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University at Zhuhai, Zhuhai 519087, China; 2. School of National Safety and Emergency Management, Beijing Normal University, Beijing 100875; 3. Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070; 4. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Re-sources Research, Chinese Academy of Sciences, Beijing 100101
  • Received:2023-10-24 Online:2024-09-20 Published:2024-09-18

Abstract:

Using the case study of a sterile-type chilling disaster during the head-flowering phase of rice, this study presents a novel approach to constructing vulnerability curves that can overcome data limitations while also taking into account crop growth mechanisms. Meteorological data during 1990-2010 was used to generate sterile-type chilling scenarios at county scale, estimated rice yield losses through combining one crop model (MCWLA) and machine learning (XGBoost) method, finally developed sterile-type chilling vulnerability curves for each main rice-planting zone in China and estimated long-term historical (1961-2010) yield loss caused by sterile-type chilling disasters. The results showed that: (1) Machine learning could effectively reproduce the estimation ability of crop model (RRMSE<6%, R2>0.93). (2) The sterile-type vulnerability decreased with decreasing latitude, and was weaker in growing seasons for late rice than that in early rice. (3) The historical yield loss was higher for single rice (1224kg·ha−1) than for double rice (early rice: 868kg·ha−1; late rice: 807kg·ha−1). 

Key words: Rice, Chilling disaster, Vulnerability, Crop model, Machine learning